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Background: Methods used to sample mosquitoes are important to consider when estimating entomologic metrics. Human landing catches (HLCs) are considered the gold standard for collecting malaria vectors. However, HLCs are labour intensive, can expose collectors to transmission risk, and are difficult to implement at scale. This study compared alternative methods to HLCs for collecting Anopheles mosquitoes in eastern Uganda. Methods: Between June and November 2021, mosquitoes were collected from randomly selected households in three parishes in Tororo and Busia districts. Mosquitoes were collected indoors and outdoors using HLCs in 16 households every 4 weeks. Additional collections were done indoors with prokopack aspirators, and outdoors with pit traps, in these 16 households every 2 weeks. CDC light trap collections were done indoors in 80 households every 4 weeks. Female Anopheles mosquitoes were identified morphologically and Anopheles gambiae sensu lato were speciated using PCR. Plasmodium falciparum sporozoite testing was done with ELISA. Results: Overall, 4,891 female Anopheles were collected, including 3,318 indoors and 1,573 outdoors. Compared to indoor HLCs, vector density (mosquitoes per unit collection) was lower using CDC light traps (4.24 vs 2.96, density ratio [DR] 0.70, 95% CIs 0.63–0.77, p < 0.001) and prokopacks (4.24 vs 1.82, DR 0.43, 95% CIs 0.37–0.49, p < 0.001). Sporozoite rates were similar between indoor methods, although precision was limited. Compared to outdoor HLCs, vector density was higher using pit trap collections (3.53 vs 6.43, DR 1.82, 95% CIs 1.61–2.05, p < 0.001), while the sporozoite rate was lower (0.018 vs 0.004, rate ratio [RR] 0.23, 95% CIs 0.07–0.75, p = 0.008). Prokopacks collected a higher proportion of Anopheles funestus (75.0%) than indoor HLCs (25.8%), while pit traps collected a higher proportion of Anopheles arabiensis (84.3%) than outdoor HLCs (36.9%). Conclusion: In this setting, the density and species of mosquitoes collected with alternative methods varied, reflecting the feeding and resting characteristics of the common vectors and the different collection approaches. These differences could impact on the accuracy of entomological indicators and estimates of malaria transmission, when using the alternative methods for sampling mosquitos, as compared to HLCs. [ABSTRACT FROM AUTHOR]

Background: Long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) are the malaria control interventions primarily responsible for reductions in transmission intensity across sub-Saharan Africa. These interventions, however, may have differential impact on Anopheles species composition and density. This study examined the changing pattern of Anopheles species in three areas of Uganda with markedly different transmission intensities and different levels of vector control. Methods: From October 2011 to June 2016 mosquitoes were collected monthly using CDC light traps from 100 randomly selected households in three areas: Walukuba (low transmission), Kihihi (moderate transmission) and Nagongera (high transmission). LLINs were distributed in November 2013 in Walukuba and Nagongera and in June 2014 in Kihihi. IRS was implemented only in Nagongera, with three rounds of bendiocarb delivered between December 2014 and June 2015. Mosquito species were identified morphologically and by PCR (Polymerase Chain Reaction). Results: In Walukuba, LLIN distribution was associated with a decline in Anopheles funestus vector density (0.07 vs 0.02 mosquitoes per house per night, density ratio [DR] 0.34, 95% CI: 0.18–0.65, p = 0.001), but not Anopheles gambiae sensu stricto (s.s.) nor Anopheles arabiensis. In Kihihi, over 98% of mosquitoes were An. gambiae s.s. and LLIN distribution was associated with a decline in An. gambiae s.s. vector density (4.00 vs 2.46, DR 0.68, 95% CI: 0.49–0.94, p = 0.02). In Nagongera, the combination of LLINs and multiple rounds of IRS was associated with almost complete elimination of An. gambiae s.s. (28.0 vs 0.17, DR 0.004, 95% CI: 0.002–0.009, p < 0.001), and An. funestus sensu lato (s.l.) (3.90 vs 0.006, DR 0.001, 95% CI: 0.0005–0.004, p < 0.001), with a less pronounced decline in An. arabiensis (9.18 vs 2.00, DR 0.15 95% CI: 0.07–0.33, p < 0.001). Conclusions: LLIN distribution was associated with reductions in An. funestus s.l. in the lowest transmission site and An. gambiae s.s. in the moderate transmission site. In the highest transmission site, a combination of LLINs and multiple rounds of IRS was associated with the near collapse of An. gambiae s.s. and An. funestus s.l. Following IRS, An. arabiensis, a behaviourally resilient vector, became the predominant species, which may have implications for malaria vector control activities. Development of interventions targeted at outdoor biting remains a priority. [ABSTRACT FROM AUTHOR]

Socioeconomic position (SEP) is an important risk factor for malaria, but there is no consensus on how to measure SEP in malaria studies. We evaluated the relative strength of four indicators of SEP in predicting malaria risk in Nagongera, Uganda. A total of 318 children resident in 100 households were followed for 36 months to measure parasite prevalence routinely every 3 months and malaria incidence by passive case detection. Household SEP was determined using: 1) two wealth indices, 2) income, 3) occupation, and 4) education. Wealth Index I (reference) included only asset ownership variables. Wealth Index II additionally included food security and house construction variables, which may directly affect malaria. In multivariate analysis, only Wealth Index II and income were associated with the human biting rate, only Wealth Indices I and II were associated with parasite prevalence, and only caregiver's education was associated with malaria incidence. This is the first evaluation of metrics beyond wealth and consumption indices for measuring the association between SEP and malaria. The wealth index still predicted malaria risk after excluding variables directly associated with malaria, but the strength of association was lower. In this setting, wealth indices, income, and education were stronger predictors of socioeconomic differences in malaria risk than occupation., (© The American Society of Tropical Medicine and Hygiene.)